Dynamic prompt creation for large language models

Abstract

The technology relates to systems and methods for dynamically generating prompts for a generative artificial intelligence (AI) model. An example method includes receiving input content for evaluation by a generative AI model; receiving an input-content embedding for the input content; receiving trait data and trait-data embeddings for the trait data; identifying similar trait data by comparing the input-content embedding with the trait-data embeddings, wherein the similar trait data is a subset of the trait data that is similar to the input content; generating a prompt including the input content and the identified similar trait data; providing the prompt to the generative AI model; and receiving, from the generative AI model in response to the prompt, an output payload including an evaluation of the input content.

What is claimed is: 
     
         1 . A system for dynamically generating prompts for a language model, the system comprising:
 at least one processor; and   memory storing instructions that, when executed by the at least one processor, cause the system to perform operations comprising:
 receive input content for evaluation by the language model; 
 receive trait data that includes pre-tagged data; 
 identify similar trait data by comparing the received trait data to the input content, wherein the similar trait data is a subset of the trait data that is similar to the input content; 
 generate a prompt including the input content and data based on the identified similar trait data; 
 provide the prompt to the language model; and 
 receive, from the language model in response to the prompt, an output payload including an evaluation of the input content. 
   
     
     
         2 . The system of  claim 1 , wherein the operation of identifying the similar trait data further comprises:
 receive an input-content embedding for the input content;   receive trait-data embeddings for the trait data; and   identify the similar trait data by comparing the trait-data embeddings with the input-content embedding.   
     
     
         3 . The system of  claim 2 , wherein comparing the trait-data embeddings with the input-content embedding comprises performing a cosine similarity analysis. 
     
     
         4 . The system of  claim 2 , wherein identifying the similar trait data by comparing the trait-data embeddings with the input-content embedding comprises:
 generate a ranked list of trait data based on a similarity of the trait-data embeddings to the input-content embedding; and   select a top N number of trait data, from the ranked list, as the similar trait data.   
     
     
         5 . The system of  claim 1 , wherein the data based on the identified similar trait data is the similar trait data and the remainder of the trait data is omitted from the prompt. 
     
     
         6 . The system of  claim 5 , wherein the data based on the identified similar trait data further comprises examples of the identified similar trait data. 
     
     
         7 . The system of  claim 1 , wherein:
 the trait data includes traits and statements;   the similar trait data includes at least one statement from a particular trait; and   the data based on the identified similar trait data includes all the statements from the particular trait and statements from other traits in the trait data are omitted from the prompt.   
     
     
         8 . The system of  claim 1 , wherein the output payload comprises relevant scores for a plurality of proposed evaluations for the input content, and the operations further comprise:
 postprocess the output payload to identify proposed evaluations that have relevant scores exceeding a threshold; and   at least one of transmit or cause display of the proposed evaluations having the relevant scores exceeding the threshold.   
     
     
         9 . The system of  claim 1 , wherein the evaluation of the input content is a classification of the input content. 
     
     
         10 . A computer-implemented method for dynamically generating prompts for a generative artificial intelligence (AI) model, the method comprising:
 receiving input content for evaluation by a generative AI model;   receiving an input-content embedding for the input content;   receiving trait data and trait-data embeddings for the trait data;   identifying similar trait data by comparing the input-content embedding with the trait-data embeddings, wherein the similar trait data is a subset of the trait data that is similar to the input content;   generating a prompt including the input content and the identified similar trait data;   providing the prompt to the generative AI model; and   receiving, from the generative AI model in response to the prompt, an output payload including an evaluation of the input content.   
     
     
         11 . The method of  claim 10 , wherein trait data other than the similar trait data is omitted from the prompt. 
     
     
         12 . The method of  claim 10 , wherein identifying the similar trait data by comparing the trait-data embeddings with the input-content embedding comprises:
 generating a ranked list of trait data based on a similarity of the trait-data embeddings to the input-content embedding; and   selecting a top N number of trait data, from the ranked list, as the similar trait data.   
     
     
         13 . The method of  claim 10 , wherein the prompt further includes examples of the identified similar trait data. 
     
     
         14 . The method of  claim 10 , wherein:
 the trait data includes categories and subcategories;   the similar trait data includes at least one subcategory from a particular category; and   the prompt further comprises all the subcategories from the particular category.   
     
     
         15 . The method of  claim 10 , wherein the output payload comprises relevant scores for a plurality of proposed evaluations for the input content, and the method further comprises postprocessing the output payload to identify proposed evaluations that have relevant scores exceeding a threshold. 
     
     
         16 . The method of  claim 10 , wherein the trait data includes data that is pre-tagged with classifications, and wherein the evaluation of the input content is a classification of the input content as one of the pre-tagged classifications. 
     
     
         17 . A computer-implemented method for dynamically generating prompts for a language model, the method comprising:
 receiving input content for classification by a language model;   requesting an embedding for the input content;   receiving, in response to the request, an input-content embedding for the input content;   receiving trait data comprising statements pre-tagged with classifications;   receiving trait-data embeddings that include embeddings of the statements;   identifying similar statements by comparing the input-content embedding with the trait-data embeddings, wherein the similar statements are the statements that are similar to the input content;   generating a prompt including the input content and the identified similar statements;   providing the prompt to the language model; and   receiving, from the language model in response to the prompt, an output payload including a classification of the input content.   
     
     
         18 . The method of  claim 17 , wherein the classification of the input content is one of the pre-tagged classifications of the similar statements. 
     
     
         19 . The method of  claim 17 , wherein the prompt does not include statements, in the trait data, that are not identified as the similar statements. 
     
     
         20 . The method of  claim 19 , wherein identifying the similar statements by comparing the trait-data embeddings with the input-content embedding comprises:
 generating a ranked list of statements based on a similarity of the trait-data embeddings to the input-content embedding; and   selecting a top N number of statements, from the ranked list, as the similar statements.